Backpropagation: The Intuition
About this video
The intuition of backpropagation: the error flows backward through the network.
Backpropagation shares out the blame for the error among all the weights of a neural network. It propagates an error signal backwards layer by layer, multiplying by the local derivatives, and so obtains the gradient of every weight in a single pass. That idea, published in 1986, is what makes training deep networks possible.
Video transcript
- 0:01 First, the forward pass:
- 0:03 the input flows through the network to the output.
- 0:06 Then the error travels backward, and the chain rule gives each weight its share,
- 0:10 layer by layer.
More videos
Explainer
Firecrawl: Web Data for Agents
If you write your own crawler: robots.txt, one queue per host and 304 Not Modified make the polite option the cheap one too.
Explainer
How to Install SearXNG with Docker
One use for your own instance: an AI agent that finds its own sources by querying SearXNG, without paying per search.
Explainer
The Computational Graph and Automatic Differentiation
The computational graph and automatic differentiation, in action.
Explainer
Backpropagation: The Step-by-Step Derivation
The backpropagation derivation, factor by factor with the chain rule.